Take-home_Ex03

Getting Started

The code chunk below will be used to install and load the necessary R packages to meet the data preparation, data wrangling, data analysis and visualisation needs.

pacman::p_load(jsonlite, tidygraph, ggraph, 
               visNetwork, graphlayouts, ggforce, 
               skimr, tidytext, tidyverse, caret, igraph, wordcloud)

Data Import

In the code chunk below, fromJSON() of jsonlite package is used to import MC3.json into R environment.

mc3_data <- fromJSON("data/MC3.json")

The output is called mc3_data. It is a large list R object.

Extracting edges

The code chunk below will be used to extract the links data.frame of mc3_data and save it as a tibble data.frame called mc3_edges.

mc3_edges <- as_tibble(mc3_data$links) %>% 
  distinct() %>%
  mutate(source = as.character(source),
         target = as.character(target),
         type = as.character(type)) %>%
  group_by(source, target, type) %>%
    summarise(weights = n()) %>%
  filter(source!=target) %>%
  ungroup()

`summarise()` has grouped output by 'source', 'target'. You can override using
the `.groups` argument.

Extracting nodes

The code chunk below will be used to extract the nodes data.frame of mc3_data and save it as a tibble data.frame called mc3_nodes.

mc3_nodes <- as_tibble(mc3_data$nodes) %>%
  mutate(country = as.character(country),
         id = as.character(id),
         product_services = as.character(product_services),
         revenue_omu = as.numeric(as.character(revenue_omu)),
         type = as.character(type)) %>%
  select(id, country, type, revenue_omu, product_services)

Warning: There was 1 warning in `mutate()`.
ℹ In argument: `revenue_omu = as.numeric(as.character(revenue_omu))`.
Caused by warning:
! NAs introduced by coercion

Initial Data Exploration

Exploring the edges data frame

In the code chunk below, skim() of skimr package is used to display the summary statistics of mc3_edges tibble data frame.

skim(mc3_edges)

Data summary
Name	mc3_edges
Number of rows	24036
Number of columns	4
_______________________
Column type frequency:
character	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
source	1	6	700	12856
target	1	6	28	21265
type	1	16	16	2

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
weights	0	1	1	0	1	1	1	1	1	▁▁▇▁▁

The report above reveals that there is not missing values in all fields.

In the code chunk below, datatable() of DT package is used to display mc3_edges tibble data frame as an interactive table on the html document.

DT::datatable(mc3_edges)

Warning in instance$preRenderHook(instance): It seems your data is too
big for client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

Initial Network Visualisation and Analysis

Building network model with tidygraph

id1 <- mc3_edges %>%
  select(source) %>%
  rename(id = source)
id2 <- mc3_edges %>%
  select(target) %>%
  rename(id = target)
mc3_nodes1 <- rbind(id1, id2) %>%
  distinct() %>%
  left_join(mc3_nodes,
            unmatched = "drop")

Joining with `by = join_by(id)`

mc3_graph <- tbl_graph(nodes = mc3_nodes1,
                       edges = mc3_edges,
                       directed = FALSE) %>%
  mutate(betweenness_centrality = centrality_betweenness(),
         closeness_centrality = centrality_closeness())

mc3_graph %>%
  filter(betweenness_centrality >= 100000) %>%
ggraph(layout = "fr") +
  geom_edge_link(aes(alpha=0.5)) +
  geom_node_point(aes(
    size = betweenness_centrality,
    colors = "lightblue",
    alpha = 0.5)) +
  scale_size_continuous(range=c(1,10))+
  theme_graph()

Warning in geom_node_point(aes(size = betweenness_centrality, colors =
"lightblue", : Ignoring unknown aesthetics: colours

Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.

Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
found in Windows font database

Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Exploring the nodes data frame

In the code chunk below, skim() of skimr package is used to display the summary statistics of mc3_nodes tibble data frame.

skim(mc3_nodes)

Data summary
Name	mc3_nodes
Number of rows	27622
Number of columns	5
_______________________
Column type frequency:
character	4
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	complete_rate	min	max	n_unique
id	1	6	64	22929
country	1	2	15	100
type	1	7	16	3
product_services	1	4	1737	3244

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
revenue_omu	21515	0.22	1822155	18184433	3652.23	7676.36	16210.68	48327.66	310612303	▇▁▁▁▁

The report above reveals that there is no missing values in all fields.

In the code chunk below, datatable() of DT package is used to display mc3_nodes tibble data frame as an interactive table on the html document.

DT::datatable(mc3_nodes)

Warning in instance$preRenderHook(instance): It seems your data is too
big for client-side DataTables. You may consider server-side processing:
https://rstudio.github.io/DT/server.html

Text Sensing with tidytext

In this section, you will learn how to perform basic text sensing using appropriate functions of tidytext package.

Simple word count

The code chunk below calculates number of times the word fish appeared in the field product_services.

mc3_nodes %>% 
    mutate(n_fish = str_count(product_services, "fish"))

# A tibble: 27,622 × 6
   id                          country type  revenue_omu product_services n_fish
   <chr>                       <chr>   <chr>       <dbl> <chr>             <int>
 1 Jones LLC                   ZH      Comp…  310612303. Automobiles           0
 2 Coleman, Hall and Lopez     ZH      Comp…  162734684. Passenger cars,…      0
 3 Aqua Advancements Sashimi … Oceanus Comp…  115004667. Holding firm wh…      0
 4 Makumba Ltd. Liability Co   Utopor… Comp…   90986413. Car service, ca…      0
 5 Taylor, Taylor and Farrell  ZH      Comp…   81466667. Fully electric …      0
 6 Harmon, Edwards and Bates   ZH      Comp…   75070435. Discount superm…      0
 7 Punjab s Marine conservati… Riodel… Comp…   72167572. Beef, pork, chi…      0
 8 Assam   Limited Liability … Utopor… Comp…   72162317. Power and Gas s…      0
 9 Ianira Starfish Sagl Import Rio Is… Comp…   68832979. Light commercia…      0
10 Moran, Lewis and Jimenez    ZH      Comp…   65592906. Automobiles, tr…      0
# ℹ 27,612 more rows

Tokenisation

The word tokenisation have different meaning in different scientific domains. In text sensing, tokenisation is the process of breaking up a given text into units called tokens. Tokens can be individual words, phrases or even whole sentences. In the process of tokenisation, some characters like punctuation marks may be discarded. The tokens usually become the input for the processes like parsing and text mining.

In the code chunk below, unnest_token() of tidytext is used to split text in product_services field into words.

token_nodes <- mc3_nodes %>%
  unnest_tokens(word, 
                product_services)

The two basic arguments to unnest_tokens() used here are column names. First we have the output column name that will be created as the text is unnested into it (word, in this case), and then the input column that the text comes from (product_services, in this case).

Now we can visualise the words extracted by using the code chunk below.

token_nodes %>%
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
      labs(x = "Count",
      y = "Unique words",
      title = "Count of unique words found in product_services field")

Selecting by n

The bar chart reveals that the unique words contains some words that may not be useful to use. For instance “a” and “to”. In the word of text mining we call those words stop words. You want to remove these words from your analysis as they are fillers used to compose a sentence.

Removing stopwords

Lucky for use, the tidytext package has a function called stop_words that will help us clean up stop words.

Let’s give this a try next!

stopwords_removed <- token_nodes %>% 
  anti_join(stop_words)

Joining with `by = join_by(word)`

Now we can visualise the words extracted by using the code chunk below.

stopwords_removed %>%
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
      labs(x = "Count",
      y = "Unique words",
      title = "Count of unique words found in product_services field")

Selecting by n

Distribution Analysis

Here is the distribution of two different types in edges.

ggplot(data = mc3_edges,
       aes(x = type)) +
  geom_bar() +
  labs(title = "Type variable in Edges")  # Add the plot title

Here is the distribution of three different types in nodes.

ggplot(data = mc3_nodes,
       aes(x = type)) +
  geom_bar() +
  labs(title = "Type variable in Nodes")  # Add the plot title

Here is the boxplot graph of revenue distribution across nodes type.

As can be seen from the graph, “Benefical Owner” has the highest median of revenue. “Company” has lots of outliers but with the lowest median of revenue. For “Company Contracts”, this category has the least variation, with the median value at around 100,000.

ggplot(data = mc3_nodes, aes(x = type, y = revenue_omu)) +
  geom_boxplot() +
  scale_y_log10() +  # Apply logarithmic scale to the y-axis
  ylab("Revenue") +
  ggtitle("Distribution of Revenue by Node Type")

Warning: Removed 21515 rows containing non-finite values (`stat_boxplot()`).

Text Visualization

This part use wordcloud library is called with the filtered words and their frequencies. The words are extracted from “product_services” to find which type of products and services are the most transported among all the goods categories. As can be seen from the graph, the words that appear at a high frequency are: fish, seafood, frozen, salmon and fresh.

library(tidytext)
library(wordcloud)

# Create a tidy text data frame from the product_services column
tidy_data <- mc3_nodes %>%
  select(product_services) %>%
  unnest_tokens(word, product_services)

# Load the stopwords dataset
data(stop_words)

# Filter out stopwords and common meaningless words
filtered_data <- tidy_data %>%
  anti_join(stop_words) %>%
  filter(!word %in% c("0", "other", "as", "a", "such", "for", "as", "the", "related", "unknown", "character"))

Joining with `by = join_by(word)`

# Compute word frequencies
word_freq <- filtered_data %>%
  count(word)

# Filter words based on frequency
min_freq <- 10
max_words <- 200
filtered_words <- word_freq %>%
  filter(n >= min_freq) %>%
  top_n(max_words, wt = n) %>%
  pull(word)

# Generate the word cloud
word_freq_filtered <- word_freq %>%
  filter(word %in% filtered_words)

# Define colors for the word cloud (darker colors)
colors <- brewer.pal(length(filtered_words), "Dark2")

Warning in brewer.pal(length(filtered_words), "Dark2"): n too large, allowed maximum for palette Dark2 is 8
Returning the palette you asked for with that many colors

wordcloud(
  words = word_freq_filtered$word,
  freq = word_freq_filtered$n,
  colors = colors
)

Network Visualization

The following network graph shows the nodes and edges that the centrality_betweenness score is larger than 100,000 and degree is above 3.

library(igraph)
library(visNetwork)

# Convert the edges graph to a tibble
edges_df <- mc3_graph %>%
  activate(edges) %>%
  as_tibble()

# Convert the nodes graph to a tibble
nodes_df <- mc3_graph %>%
  activate(nodes) %>%
  as_tibble() %>%
  rename(label = id) %>%
  mutate(id = row_number()) %>%
  select(id, label)

# Perform community detection using the Louvain algorithm on the graph
communities <- cluster_louvain(mc3_graph)

# Get the cluster membership of each node
membership <- membership(communities)

# Add the cluster membership information to the nodes data frame
nodes_df$group <- membership

mc3_graph <- tbl_graph(nodes = mc3_nodes1,
                       edges = mc3_edges,
                       directed = FALSE) %>%
mutate(betweenness_centrality = centrality_betweenness(),
       closeness_centrality = as.factor(centrality_closeness())) %>%
filter(betweenness_centrality >= 100000)

         
# Calculate the degrees of each node
degrees <- degree(mc3_graph)
set.seed (1234)

mc3_graph %>%
  # filter(betweenness_centrality >= 100000) %>%
  ggraph(layout = "fr") +
  geom_edge_link(aes(alpha = 0.5)) +
  geom_node_point(aes(size = betweenness_centrality,
                      color = closeness_centrality,
                      alpha = 0.5), show.legend = FALSE) +
  geom_node_text(aes(label = ifelse(degrees > 3, as.character(id), "")), size = 2) +  # Add node labels
  scale_size_continuous(range = c(1, 10)) +
  labs(title = "Network Visualization") +  # Add the plot title
  theme_graph()

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

This code plots the graph of network visualization to show the nodes and edges of “beneficial owner” and “company”.

library(ggraph)

mc3_graph %>%
  filter(betweenness_centrality >= 1000000) %>%
  ggraph(layout = "fr") +
  geom_edge_link(aes(alpha = 0.5)) +
  geom_node_point(aes(
    size = betweenness_centrality,
    color = type,
    shape = type),
    alpha = 0.8) +
  scale_size_continuous(range = c(1, 6)) +
  scale_color_manual(values = c("Company Contacts" = "yellow", "Beneficial Owner" = "red", "Company" = "blue")) +
  scale_shape_manual(values = c("Company Contacts" = 17, "Beneficial Owner" = 15, "Company" = 16)) +
  theme_graph() +
  labs(title = "Network Visualization") +
  theme(plot.title = element_text(hjust = 0.5))

Warning: Removed 34 rows containing missing values (`geom_point()`).

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
family not found in Windows font database

Group Detection

edges_df <- mc3_graph %>%
  activate(edges) %>%
  as.tibble()

Warning: `as.tibble()` was deprecated in tibble 2.0.0.
ℹ Please use `as_tibble()` instead.
ℹ The signature and semantics have changed, see `?as_tibble`.

nodes_df <- mc3_graph %>%
  activate(nodes) %>%
  as.tibble() %>%
  rename(label = id) %>%
  mutate(id=row_number()) %>%
  select(id, label)

# Perform community detection using the cluster edge betweenness
communities <- cluster_edge_betweenness(mc3_graph)

# Get the cluster membership of each node
membership <- membership(communities)

# Add the cluster membership information to the nodes data frame
nodes_df$group <- membership

# Plot the graph with clustered nodes using visNetwork
visNetwork(nodes_df, edges_df) %>%
  visIgraphLayout(layout = "layout_with_fr") %>%
  visEdges(arrows = "to",
           smooth = list(enabled = TRUE,
                         type = "curvedCW"), 
           color = list(highlight = "lightgray")) %>%
  visOptions(highlightNearest = list(enabled = TRUE,
                                     degree = 1,
                                     hover = TRUE,
                                     labelOnly = TRUE),
             nodesIdSelection = TRUE,
             selectedBy = "group") %>%
  visLayout(randomSeed = 1234)